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TM 745 Forecasting for Business & Technology Dr. Frank Joseph Matejcik. 3rd Session 2/11/08: Chapter 3 Moving Averages and Exponential Smoothing . South Dakota School of Mines and Technology, Rapid City . Agenda & New Assignment. ch3(1,5,8,11) Tentative Schedule
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TM 745 Forecasting for Business & TechnologyDr. Frank Joseph Matejcik 3rd Session 2/11/08: Chapter 3 Moving Averages and Exponential Smoothing South Dakota School of Mines and Technology, Rapid City
Agenda & New Assignment • ch3(1,5,8,11) Tentative Schedule • Chapter 3 WK (with odd diversions) • Try to use ForecastX for Autocorrelation • Business Forecasting 5th Edition J. Holton Wilson & Barry KeatingMcGraw-Hill
Tentative Schedule Chapters Assigned 28-Jan 1 problems 1,4,8 e-mail, contact 4-Feb 2 problems 4, 8, 9 11-Feb 3 problems 1,5,8,11 18-Feb President’s Day 25-Feb 4 problems 6,10 3-Mar 5 problems 5,8 10-Mar Exam 1 Ch 1-4 Revised 17-Mar Break 24-Mar Easter 31-Mar 6 problems 4, 7 Chapters Assigned 7-Apr 7 3,4,5(series A) 7B 21-Apr 8 Problem 6 28-Apr 9 05-May Final
Web Resources • Class Web site on the HPCnet system • http://sdmines.sdsmt.edu/sdsmt/directory/courses/2008sp/tm745M021 • Streaming video http://its.sdsmt.edu/Distance/ • Answers will be online. Linked from ^ • The same class session that is on the DVD is on the stream in lower quality. http://www.flashget.com/ will allow you to capture the stream more readily and review the lecture, anywhere you can get your computer to run.
Moving Averages & Exponential Smoothing • All basic methods based on smoothing • 1. Moving averages • 2. Simple exponential smoothing • 3. Holt's exponential smoothing • 4. Winters' exponential smoothing • 5. Adaptive-response-rate single exponential smoothing
Moving Averages • Ex. “Three Quarter Moving Average”(1999Q1+1999Q2+1999Q3)/3 =Forecast for 1999Q4 • Slutsky-Yule effect: Any moving average could appear to be acycle, because it is a serially correlated set of random numbers.
Simple Exponential Smoothing • Alternative interpretation
Simple Exponential Smoothing • Why they call it exponential property
Simple Exponential Smoothing • Advantages • Simpler than other forms • Requires limited data • Disdvantages • Lags behind actual data • No trend or seasonality
ForecastXTM Conventions forSmoothing Constants • Alpha (a) =the simple smoothing constant • Gamma (g) =the trend smoothing constant • Beta (b) =the seasonality smoothing constant
Holt's Exponential Smoothing • ForecastX will pick the smoothing constants to minimize RMSE • Some trend, but no seasonality • Call it linear trend smoothing
Adaptive-Response-Rate Single Exponential Smoothing • Adaptive is a clue to how it works • No direct way of handling seasonality • Does not handle trends • ForecastX has different algorithm
Using Single, Holt's, or ADRES Smoothing to Forecast a Seasonal Data Series • 1. Calculate seasonal indices for the series. Done in HOLT WINTERS ForecastX™. • 2. Deseasonalize the original data by dividing each value by its corresponding seasonal index.
Using Single, Holt's, or ADRES Smoothing to Forecast a Seasonal Data Series • 3. Apply a forecasting method (such as ES, Holt's, or ADRES) to the deseasonalized series to produce an intermediate forecast of the deseasonalized data. • 4. Reseasonalize the series by multiplying each deseasonalized forecast by its corresponding seasonal index.
Event Modeling • Event Indices Legend 0. No event present • Free-standing inserts (FSIs) • FSI/radio, television, print campaign • Load (trade promotion) • Deload (month after effect of load) • Thematics (themed adg campaign) • Instant redeemable coupon (IRC)
Summary • All basic methods based on smoothing • 1. Moving averages • 2. Simple exponential smoothing • 3. Holt's exponential smoothing • 4. Winters' exponential smoothing • 5. Adaptive-response-rate single exponential smoothing • Use of Deseasonalized Series • techniques not clear winners